Techniques for deep reinforcement learning have matured rapidly over the last few years, yielding high levels of performance in tasks ranging from chess and Go [1]} to realtime strategy [2]}, [3]} to first person 3D games [4]}, [5]}. However, despite these successes, poor sample efficiency, generalization, and transfer remain widely acknowledged pitfalls. To address those challenges, there has recently been growing interest in the topic of meta-learning [6]}, [7]}, and how meta-learning abilities can be integrated into deep RL agents [8]}, [9]}. Although a bevy of interesting and innovative techniques for meta-reinforcement learning have been proposed [10]}, [11]}, [12]}, [13]}, research in this area has been hindered by a `problem problem,' that is, a dearth of ideal task benchmarks. In the present work, we contribute toward a remedy, by introducing and publicly releasing a new and principled benchmark for meta-RL research.
